Systematic Evidence Synthesis for Non-Invasive Hemoglobin Estimation Using Multispectral Photoplethysmography and Interpretable AI

Abstract

Over 1.6 billion people worldwide suffer from anemia, with low-resource groups, pregnant women, and small children the most affected. Invasive venipuncture and laboratory spectrophotometry are used in standard diagnosis; these procedures are uncomfortable, involve a risk of infection, and need infrastructure that restricts repeated testing, especially in rural and isolated areas. This paper examines current developments in continuous non-invasive hemoglobin monitoring, highlighting multi-wavelength photoplethysmography that spans visible bands from 660 to 730 nm and near-infrared bands from 850 to 940 nm, together with machine learning regression models. Building on the Beer-Lambert law, we compare traditional chemometric techniques with gradient-boosted trees and deep neural networks, which capture nonlinear correlations in multichannel signals. Additionally, we evaluate the best reported predictive performance, with coefficients of determination between 0.85 and 0.997 and mean absolute errors from 0.3 to 8.5 grams per liter across the surveyed studies, and subject-level screening that reaches a sensitivity of 93.3 percent and a specificity of 93.8 percent. We look at sensor possibilities such as CMOS image sensors, solar cells, and photodiode arrays, and discuss explainable artificial intelligence techniques such as SHAP that allow clinical validation and make model outputs interpretable. Lastly, we outline strategies for creating dependable, equitable wearable screening tools appropriate for routine, community-level, and quick point-of-care anemia detection. We also highlight the main technical and clinical obstacles to adoption, such as skin tone bias, motion artifacts, data leakage, and variability across populations.